{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:03:13Z","timestamp":1782313393036,"version":"3.54.5"},"reference-count":22,"publisher":"Impact Journals, LLC","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Oncotarget"],"published-print":{"date-parts":[[2021,6,8]]},"DOI":"10.18632\/oncotarget.27979","type":"journal-article","created":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T13:48:45Z","timestamp":1622641725000},"page":"1187-1196","source":"Crossref","is-referenced-by-count":17,"title":["Deep learning with deep convolutional neural network using FDG-PET\/CT for malignant pleural mesothelioma diagnosis"],"prefix":"10.18632","volume":"12","author":[{"given":"Kazuhiro","family":"Kitajima","sequence":"first","affiliation":[{"name":"Department of Radiology, Hyogo College of Medicine, Nishinomiya, Hyogo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hidetoshi","family":"Matsuo","sequence":"additional","affiliation":[{"name":"Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atsushi","family":"Kono","sequence":"additional","affiliation":[{"name":"Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kozo","family":"Kuribayashi","sequence":"additional","affiliation":[{"name":"Division of Respiratory Medicine, Department of Internal Medicine, Hyogo College of Medicine, Nishinomiya, Hyogo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takashi","family":"Kijima","sequence":"additional","affiliation":[{"name":"Division of Respiratory Medicine, Department of Internal Medicine, Hyogo College of Medicine, Nishinomiya, Hyogo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masaki","family":"Hashimoto","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Hyogo College of Medicine, Nishinomiya, Hyogo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seiki","family":"Hasegawa","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Hyogo College of Medicine, Nishinomiya, Hyogo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takamichi","family":"Murakami","sequence":"additional","affiliation":[{"name":"Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koichiro","family":"Yamakado","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hyogo College of Medicine, Nishinomiya, Hyogo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7892","published-online":{"date-parts":[[2021,6,8]]},"reference":[{"key":"1","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1164\/rccm.200811-1729OC","article-title":"Clinical impact and reliability of pleural fluid mesothelin in undiagnosed pleural effusions","volume":"18","author":"Lee","year":"2009","journal-title":"Am J Respir Crit Care Med"},{"key":"2","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1159\/000443483","article-title":"CT Scan-Guided Abrams' Needle Pleural Biopsy versus Ultrasound-Assisted Cutting Needle Pleural Biopsy for Diagnosis in Patients with Pleural Effusion: A Randomized, Controlled Trial","volume":"9","author":"Metintas","year":"2016","journal-title":"Respiration"},{"key":"3","doi-asserted-by":"publisher","first-page":"89","DOI":"10.5858\/arpa.2017-0124-RA","article-title":"Guidelines for Pathologic Diagnosis of Malignant Mesothelioma 2017 Update of the Consensus Statement From the International Mesothelioma Interest Group","volume":"14","author":"Gown","year":"2018","journal-title":"Arch Pathol Lab Med"},{"key":"4","first-page":"1144","article-title":"Assessment of malignant pleural mesothelioma with (18)F-FDG dual-head gamma-camera coincidence imaging: comparison with histopathology","volume":"4","author":"Treves","year":"2002","journal-title":"J Nucl Med"},{"key":"5","first-page":"995","article-title":"PET for the evaluation of pleural thickening observed on CT","volume":"4","author":"Groen","year":"2004","journal-title":"J Nucl Med"},{"key":"6","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1378\/chest.125.2.489","article-title":"Evaluation of pleural disease with 18-fluorodeoxyglucose positron emission tomography imaging","volume":"12","author":"Bury","year":"2004","journal-title":"Chest"},{"key":"7","doi-asserted-by":"publisher","first-page":"1480","DOI":"10.1097\/JTO.0b013e3181c0a7ff","article-title":"Clinical value of fluorodeoxyglucose-positron emission tomography\/computed tomography in differentiation of malignant mesothelioma from asbestos-related benign pleural disease: an observational pilot study","volume":"4","author":"Erginel","year":"2009","journal-title":"J Thorac Oncol"},{"key":"8","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1055\/s-2008-1039314","article-title":"The role of positron emission tomography\/computed tomography in the diagnosis of pleural diseases","volume":"5","author":"Kutlu","year":"2009","journal-title":"Thorac Cardiovasc Surg"},{"key":"9","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.lungcan.2012.10.017","article-title":"F-fluoro-2-deoxy-d-glucose positron emission tomography\/computed tomography fused imaging in malignant mesothelioma patients: looking from outside is not enough","volume":"7","author":"Astoul","year":"2013","journal-title":"Lung Cancer"},{"key":"10","doi-asserted-by":"publisher","first-page":"e0161764","DOI":"10.1371\/journal.pone.0161764","article-title":"The Role ofF-FDG PET\/CT Integrated Imaging in Distinguishing Malignant from Benign Pleural Effusion","volume":"1","author":"Lu","year":"2016","journal-title":"PLoS One"},{"key":"11","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1378\/chest.14-0820","article-title":"Accuracy of fluorodeoxyglucose-PET imaging for differentiating benign from malignant pleural effusions: a meta-analysis","volume":"14","author":"Salud","year":"2015","journal-title":"Chest"},{"key":"12","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1007\/s11604-016-0555-1","article-title":"Present and future roles of FDG-PET\/CT imaging in the management of malignant pleural mesothelioma","volume":"3","author":"Kuribayashi","year":"2016","journal-title":"Jpn J Radiol"},{"key":"13","doi-asserted-by":"publisher","first-page":"2113","DOI":"10.1148\/rg.2017170077","article-title":"Deep Learning: A Primer for Radiologists","volume":"3","author":"Tang","year":"2017","journal-title":"Radiographics"},{"key":"14","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/s11604-018-0726-3","article-title":"Deep learning with convolutional neural network in radiology","volume":"3","author":"Abe","year":"2018","journal-title":"Jpn J Radiol"},{"key":"15","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"54","author":"Thrun","year":"2017","journal-title":"Nature"},{"key":"16","doi-asserted-by":"publisher","first-page":"101858","DOI":"10.1016\/j.media.2020.101858","article-title":"Convolutional neural networks for the automatic diagnosis of melanoma: An extensive experimental study","volume":"6","author":"Ventura","year":"2021","journal-title":"Med Image Anal"},{"key":"17","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation","volume":"3","author":"Glocker","year":"2017","journal-title":"Med Image Anal"},{"key":"18","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s13550-017-0260-9","article-title":"Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer fromF-FDG PET\/CT images","volume":"7","author":"Yu","year":"2017","journal-title":"EJNMMI Res"},{"key":"19","doi-asserted-by":"publisher","first-page":"6741","DOI":"10.1007\/s00330-019-06265-x","article-title":"Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [F]-fluorodeoxyglucose positron emission tomography\/computed tomography","volume":"2","author":"Kao","year":"2019","journal-title":"Eur Radiol"},{"key":"20","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1186\/s13550-020-00669-3","article-title":"FDG PET versus CT radiomics to predict outcome in malignant pleural mesothelioma patients","volume":"1","author":"Opitz","year":"2020","journal-title":"EJNMMI Res"},{"key":"21","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","article-title":"Places: A 10 Million Image Database for Scene Recognition","volume":"4","author":"Torralba","year":"2018","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"22","doi-asserted-by":"publisher","first-page":"1661","DOI":"10.1007\/s00259-018-4008-1","article-title":"Response to neoadjuvant chemotherapy for breast cancer judged by PERCIST - multicenter study in Japan","volume":"4","author":"Miyoshi","year":"2018","journal-title":"Eur J Nucl Med Mol Imaging"}],"container-title":["Oncotarget"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.oncotarget.com\/lookup\/doi\/10.18632\/oncotarget.27979","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T16:49:35Z","timestamp":1625762975000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.oncotarget.com\/lookup\/doi\/10.18632\/oncotarget.27979"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,8]]},"references-count":22,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2021,6,8]]}},"alternative-id":["27979","34136087"],"URL":"https:\/\/doi.org\/10.18632\/oncotarget.27979","relation":{},"ISSN":["1949-2553"],"issn-type":[{"value":"1949-2553","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,8]]}}}